Adaptive filter theory (3rd ed.)
Adaptive filter theory (3rd ed.)
Iterative Methods for Sparse Linear Systems
Iterative Methods for Sparse Linear Systems
Analysis of conjugate gradient algorithms for adaptive filtering
IEEE Transactions on Signal Processing
Conjugate gradient method in adaptive bilinear filtering
IEEE Transactions on Signal Processing
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The performance of the modified adaptive conjugate gradient (CG) algorithms based on the iterative CG method for adaptive filtering is highly related to the ways of estimating the correlation matrix and the cross-correlation vector. The existing approaches of implementing the CG algorithms using the data windows of exponential form or sliding form result in either loss of convergence or increase in misadjustment. This paper presents and analyzes a new approach to the implementation of the CG algorithms for adaptive filtering by using a generalized data windowing scheme. For the new modified CG algorithms, we show that the convergence speed is accelerated, the misadjustment and tracking capability comparable to those of the recursive least squares (RLS) algorithm are achieved. Computer simulations demonstrated in the framework of linear system modeling problem show the improvements of the new modifications.